Data from experiments and direct simulations of turbulence have historically been used to
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …

In computational fluid dynamics simulations of industrial flows, models based on the
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …

Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering
turbulent flow simulations. However, RANS predictions may have large discrepancies due to …

This work presents a review and perspectives on recent developments in the use of machine
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …

While data-based approaches were found to be useful for subgrid scale (SGS) modeling in
Reynolds-averaged Navier-Stokes (RANS) simulations, there have not been many attempts …

This paper presents a novel CFD-driven machine learning framework to develop Reynolds-
averaged Navier-Stokes (RANS) models. The CFD-driven training is an extension of the …

Reynolds-averaged Navier–Stokes (RANS) simulations with turbulence closure models
continue to play important roles in industrial flow simulations. However, the commonly used …

Data-driven methods for improving turbulence modeling in Reynolds-Averaged Navier–
Stokes (RANS) simulations have gained significant interest in the computational fluid …

Direct numerical simulations of channel flow up to R e τ= 8000 have been performed to
determine the existence of a logarithmic region in channel flow at high-Reynolds number. It …

A novel machine learning algorithm is presented, serving as a data-driven turbulence
modeling tool for Reynolds Averaged Navier-Stokes (RANS) simulations. This machine …